Design and development of drugs for Alzheimer’s dementia as a protein misfolding disorder
Bibliographic record
Abstract
Background: There are no disease modifying agents for the treatment of Alzheimer’s disease (AD). Pathologically, AD is associated with the misfolding of two peptides: beta-amyloid (plaques) and tau (tangles). Methods: Using large-scale computer simulations, we modelled the misfolding of both beta-amyloid and tau, identifying a common conformational motif (CCM; i.e. an abnormal peptide shape), present in both beta-amyloid and tau, that promotes their misfolding. We screened a library of 11.8 million compounds against this in silico model of protein misfolding, identifying three novel molecular classes of putative therapeutics as anti-protein misfolding agents. We synthesized approximately 400 new chemical entity drug-like molecules in each of these three classes (i.e. 1200 potential drug candidates). These were comprehensively screened in a battery of five in vitro protein oligomerization assays. Selected compounds were next evaluated in the APP/PS1 doubly transgenic mouse model of AD. Results: Two new classes of molecules were identified with the ability to block the oligomerization of both beta-amyloid and tau. These compounds are drug-like with good pharmacokinetic properties and are brain-penetrant. They exhibit excellent efficacy in transgenic mouse models. Conclusion: Computer aided drug design has enabled the discovery of novel drug-like molecules able to inhibit both tau and beta-amyloid misfolding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".